Schedule

Here’s your roadmap for the year!

Every class session has four important sections. You should read about the details for each using the main menu at the top of this webpage.

Week Overview (): This page overviews the weeks
Lectures (): This page is for lectures
Labs (): This page is about labs
Extras (): This page is about Extras
Coursework (): This page contains the instructions for either the session exercise (1–3 brief tasks), or for the two mini projects and final project. Assignments are due by 11:59 PM on the day they’re listed.
Mini-Diss (): This page contains an interactive lesson that teaches you the principles and code you need to know. Go through these after doing the content.

tl;dr: You should follow this general process each week:

Check out the overview () page and do any thinking/reading/prep suggested if you have the chance
Come to the lecture () page
Attend the lab () and get cracking
Use the plentiful Mini-Diss resources () to help you smash the MD

https://littlemonkeylab.com/researchmethods/weeks/week02.html

Weekly schedule

week	date	what	topic	notes
1	Monday , 02 October	Lecture01	No lab
	Tuesday , 03 October	Lab01	Welcome to STA 199	Here is some deadline information!
2	Monday , 09 October	Lecture02	Hello R!
	Tuesday , 10 October	Lab02	Grammar of graphics	And something spicy here too!
3	Monday , 16 October	Lecture03	Visualizing various types of data
	Tuesday , 17 October	Lab03
4	Monday , 23 October	Lecture04
	Tuesday , 24 October	Lab04
5	Monday , 30 October	Lecture05
	Tuesday , 31 October	Lab05
Reading Week
6	Monday , 13 November	Lecture06	Data visualization
	Tuesday , 14 November	Lab06	Grammar of data wrangling
7	Monday , 20 November	Lecture07	Working with multiple data frames
	Tuesday , 21 November	Lab07
8	Monday , 27 November	Lecture08	Data wrangling
	Tuesday , 28 November	Lab08	Tidying data
9	Monday , 04 December	Lecture09	Data types and classes
	Tuesday , 05 December	Lab09
10	Monday , 11 December	Lecture10	Data tidying
	Tuesday , 12 December	Lab10	Importing and recoding data
Winter Break
11	Monday , 08 January	Lecture11
	Tuesday , 09 January	Lab11	No lab - Work on Eam 1
12	Monday , 15 January	Lecture12	Data science ethics - Misrepresentation
	Tuesday , 16 January	Lab12	Data science ethics - Algorithmic bias + data privacy
13	Monday , 22 January	Lecture13	No lab - Fall break
	Tuesday , 23 January	Lab13	No Lec - Fall break
14	Monday , 29 January	Lecture14	Web scraping
	Tuesday , 30 January	Lab14	Work on project proposal
15	Monday , 05 February	Lecture15	Functions + iteration
	Tuesday , 06 February	Lab15	The language of models
Reading Week
16	Monday , 19 February	Lecture16	Probability + Simpson's Parado
	Tuesday , 20 February	Lab16	Models with a single predictor
17	Monday , 26 February	Lecture17	Models with multiple predictors
	Tuesday , 27 February	Lab17
18	Monday , 04 March	Lecture18	Predicting a numerical outcome
	Tuesday , 05 March	Lab18	Models with multiple predictors + Overfitting
19	Monday , 11 March	Lecture19
	Tuesday , 12 March	Lab19
20	Monday , 18 March	Lecture20
	Tuesday , 19 March	Lab20

Week	Dates	Topic